Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

# !rm data/celeba.zip
# tf.reset_default_graph()

import math
import os

import hashlib
from urllib.request import urlretrieve
import zipfile
import gzip
import shutil

import numpy as np
from PIL import Image
from tqdm import tqdm

from copy import deepcopy
from unittest import mock

from distutils.version import LooseVersion
import warnings

import tensorflow as tf

from glob import glob
from matplotlib import pyplot
from matplotlib import cm

def _read32(bytestream):
    """Read 32-bit integer from bytesteam
       :param bytestream: A bytestream
       :return: 32-bit integer"""
    dt = np.dtype(np.uint32).newbyteorder('>')
    return np.frombuffer(bytestream.read(4), dtype=dt)[0]

def _unzip(save_path, _, database_name, data_path):
    """Unzip wrapper with the same interface as _ungzip
       :param save_path: The path of the gzip files
       :param database_name: Name of database
       :param data_path: Path to extract to
       :param _: HACK - Used to have to same interface as _ungzip"""
    print('Extracting {}...'.format(database_name))
    with zipfile.ZipFile(save_path) as zf:
        zf.extractall(data_path)

def _ungzip(save_path, extract_path, database_name, _):
    """Unzip a gzip file and extract it to extract_path
       :param save_path: The path of the gzip files
       :param extract_path: The location to extract the data to
       :param database_name: Name of database
       :param _: HACK - Used to have to same interface as _unzip"""
    # Get data from save_path
    with open(save_path, 'rb') as f:
        with gzip.GzipFile(fileobj=f) as bytestream:
            magic = _read32(bytestream)
            if magic != 2051:
                raise ValueError('Invalid magic number {} in file: {}'.format(magic, f.name))
            num_images = _read32(bytestream)
            rows = _read32(bytestream)
            cols = _read32(bytestream)
            buf = bytestream.read(rows * cols * num_images)
            data = np.frombuffer(buf, dtype=np.uint8)
            data = data.reshape(num_images, rows, cols)

    # Save data to extract_path
    for image_i, image in enumerate(
            tqdm(data, unit='File', unit_scale=True, miniters=1, desc='Extracting {}'.format(database_name))):
        Image.fromarray(image, 'L').save(os.path.join(extract_path, 'image_{}.jpg'.format(image_i)))  
        
def get_image(image_path, width, height, mode):
    """Read image from image_path
       :param image_path: Path of image
       :param width: Width of image
       :param height: Height of image
       :param mode: Mode of image
       :return: Image data"""
    image = Image.open(image_path)

    if image.size != (width, height):  # HACK - Check if image is from the CELEBA dataset
        # Remove most pixels that aren't part of a face
        face_width = face_height = 108
        j = (image.size[0] - face_width) // 2
        i = (image.size[1] - face_height) // 2
        image = image.crop([j, i, j + face_width, i + face_height])
        image = image.resize([width, height], Image.BILINEAR)

    return np.array(image.convert(mode))

def get_batch(image_files, width, height, mode):
    data_batch = np.array(
        [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)

    # Make sure the images are in 4 dimensions
    if len(data_batch.shape) < 4:
        data_batch = data_batch.reshape(data_batch.shape + (1,))

    return data_batch
  
def images_square_grid(images, mode):
    """Save images as a square grid
       :param images: Images to be used for the grid
       :param mode: The mode to use for images
       :return: Image of images in a square grid"""
    # Get maximum size for square grid of images
    save_size = math.floor(np.sqrt(images.shape[0]))

    # Scale to 0-255
    images = (((images - images.min()) * 255) / (images.max() - images.min())).astype(np.uint8)

    # Put images in a square arrangement
    images_in_square = np.reshape(
            images[:save_size*save_size],
            (save_size, save_size, images.shape[1], images.shape[2], images.shape[3]))
    if mode == 'L':
        images_in_square = np.squeeze(images_in_square, 4)

    # Combine images to grid image
    new_im = Image.new(mode, (images.shape[1] * save_size, images.shape[2] * save_size))
    for col_i, col_images in enumerate(images_in_square):
        for image_i, image in enumerate(col_images):
            im = Image.fromarray(image, mode)
            new_im.paste(im, (col_i * images.shape[1], image_i * images.shape[2]))

    return new_im
  
def download_extract(database_name, data_path):
    """Download and extract database
       :param database_name: Database name"""
    DATASET_CELEBA_NAME = 'celeba'
    DATASET_MNIST_NAME = 'mnist'

    if database_name == DATASET_CELEBA_NAME:
        url = 'https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/celeba.zip'
        hash_code = '00d2c5bc6d35e252742224ab0c1e8fcb'
        extract_path = os.path.join(data_path, 'img_align_celeba')
        save_path = os.path.join(data_path, 'celeba.zip')
        extract_fn = _unzip
    elif database_name == DATASET_MNIST_NAME:
        url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
        hash_code = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
        extract_path = os.path.join(data_path, 'mnist')
        save_path = os.path.join(data_path, 'train-images-idx3-ubyte.gz')
        extract_fn = _ungzip

    if os.path.exists(extract_path):
        print('Found {} Data'.format(database_name))
        return

    if not os.path.exists(data_path):
        os.makedirs(data_path)

    if not os.path.exists(save_path):
        with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Downloading {}'.format(database_name)) as pbar:
            urlretrieve(
                url,
                save_path,
                pbar.hook)

    assert hashlib.md5(open(save_path, 'rb').read()).hexdigest() == hash_code, \
        '{} file is corrupted.  Remove the file and try again.'.format(save_path)

    os.makedirs(extract_path)
    try:
        extract_fn(save_path, extract_path, database_name, data_path)
    except Exception as err:
        shutil.rmtree(extract_path)  # Remove extraction folder if there is an error
        raise err

    # Remove compressed data
    os.remove(save_path)
    
class Dataset(object):
    def __init__(self, dataset_name, data_files):
        """Initalize the class
           :param dataset_name: Database name
           :param data_files: List of files in the database"""
        DATASET_CELEBA_NAME = 'celeba'
        DATASET_MNIST_NAME = 'mnist'
        IMAGE_WIDTH = 28
        IMAGE_HEIGHT = 28

        if dataset_name == DATASET_CELEBA_NAME:
            self.image_mode = 'RGB'
            image_channels = 3

        elif dataset_name == DATASET_MNIST_NAME:
            self.image_mode = 'L'
            image_channels = 1

        self.data_files = data_files
        self.shape = len(data_files), IMAGE_WIDTH, IMAGE_HEIGHT, image_channels

    def get_batches(self, batch_size):
        """Generate batches
          :param batch_size: Batch Size
          :return: Batches of data"""
        IMAGE_MAX_VALUE = 255

        current_index = 0
        while current_index + batch_size <= self.shape[0]:
            data_batch = get_batch(
                self.data_files[current_index:current_index + batch_size],
                *self.shape[1:3],
                self.image_mode)

            current_index += batch_size

            yield data_batch / IMAGE_MAX_VALUE - 0.5
            
class DLProgress(tqdm):
    """Handle Progress Bar while Downloading"""
    last_block = 0

    def hook(self, block_num=1, block_size=1, total_size=None):
        """A hook function that will be called once on establishment of the network connection 
           and once after each block read thereafter.
           :param block_num: A count of blocks transferred so far
           :param block_size: Block size in bytes
           :param total_size: The total size of the file. This may be -1 on older FTP servers 
                              which do not return a file size in response to a retrieval request."""
        self.total = total_size
        self.update((block_num - self.last_block) * block_size)
        self.last_block = block_num

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

download_extract('mnist', data_dir)
download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f54d5450a58>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 50

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f54dcdb2518>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.13.1
Default GPU Device: /device:GPU:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, 
                                shape=[None, image_width, image_height, image_channels],
                                name="Real_Input")
    input_z = tf.placeholder(tf.float32, 
                                shape=[None, z_dim],
                                name="Z_Input") 
    input_lr = tf.placeholder(tf.float32, 
                                shape=[],
                                name="Learning_Rate") 
    return input_real, input_z, input_lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, 
                              padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        relu1 = tf.maximum(alpha * x1, x1)
        print ('L1:',relu1.shape)

        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, 
                              padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        print ('L2:',relu2.shape)

        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, 
                              padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        print ('L3:',relu3.shape)
        
        x4 = tf.layers.conv2d(relu3, 512, 5, strides=2, 
                              padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        bn4 = tf.layers.batch_normalization(x4, training=True)
        print ('L4:',bn4.shape)

        # Flatten it
        flat = tf.reshape(bn4, (-1, 2*2*512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        print ('L5:',out.shape)

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True, alpha=0.2, training=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        bn1 = tf.layers.batch_normalization(x1, training=training)
        # leaky_relu activation
        relu1 = tf.nn.leaky_relu(bn1,alpha=0.2,name='relu1')

        print ('\nDL1:',relu1.shape)

        x2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides=2, 
                                        padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        bn2 = tf.layers.batch_normalization(x2, training=training)
        # leaky_relu activation
        relu2 = tf.nn.leaky_relu(bn2,alpha=0.2,name='relu2')
        
        print ('DL2:',relu2.shape)
            
        x3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, 
                                        padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))
        bn3 = tf.layers.batch_normalization(x3, training=training)
        # leaky_relu activation
        relu3 = tf.nn.leaky_relu(bn3,alpha=0.2,name='relu3')

        print ('DL3:',relu3.shape)

        # Output layer
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 1, strides=1, 
                                            padding='same', kernel_initializer=tf.random_normal_initializer(stddev=0.1))

        print ('DL4:',logits.shape)        
        
        out = tf.tanh(logits)
    return out
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 5)

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 5)
Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    d_labels_real = tf.ones_like(d_model_real)
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=d_labels_real))

    d_labels_fake = tf.zeros_like(d_model_fake)
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                                        labels=d_labels_fake))
    
    g_labels = tf.ones_like(d_model_fake)
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=g_labels))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 4)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

class GAN:
    def __init__(self, real_size, z_dim, learning_rate, alpha=0.2, beta1=0.5):

        self.input_real, self.input_z, self.learning_rate = model_inputs(*real_size, 
                                                                         z_dim)
        
        self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z,
                                                      real_size[2], alpha=alpha)
        
        self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, 
                                           learning_rate, beta1)

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, train_step=0, alpha = 0.2):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    train_step = 0
    DTL, GTL = [], []
    # Create the network
    net = GAN(data_shape[1:], z_dim, learning_rate, alpha=alpha, beta1=beta1)
    print_step = 50
    show_step = 500
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                train_step += 1
                batch_images *= 2.0                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(net.d_opt, 
                             feed_dict={net.input_real: batch_images, net.input_z: batch_z})
                _ = sess.run(net.g_opt, 
                             feed_dict={net.input_z: batch_z, net.input_real: batch_images})

                if train_step % print_step == 0:
                    discriminator_training_loss = net.d_loss.eval({net.input_real: batch_images, 
                                                                   net.input_z: batch_z})
                    generator_training_loss = net.g_loss.eval({net.input_z: batch_z,
                                                              net.input_real: batch_images})

                    print("Epoch {}/{}| Step {}|".format(epoch_i + 1, epochs, train_step),
                          "Discriminator Loss:{:.5f}|".format(discriminator_training_loss),
                          "Generator Loss:{:.5f}|".format(generator_training_loss),
                          "Discriminator Loss>Generator Loss: {}"\
                          .format(discriminator_training_loss > generator_training_loss))
                    
                    DTL.append(discriminator_training_loss) 
                    GTL.append(generator_training_loss) 
                    
                if train_step % show_step == 0:
                    show_generator_output(sess, 28, net.input_z, data_shape[3], data_image_mode)
                    
        show_generator_output(sess, 28, net.input_z, data_shape[3], data_image_mode)
        
    pyplot.figure(figsize=(18, 6))
    pyplot.plot(DTL, label = 'discriminator')
    pyplot.plot(GTL, label = 'generator')
    pyplot.legend()
    pyplot.title('Loss Function');

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 128
learning_rate = 0.001
beta1 = 0.2

epochs = 25
alpha = 0.2
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""

mnist_dataset = Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, alpha=alpha)

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
Epoch 1/25| Step 50| Discriminator Loss:0.50208| Generator Loss:12.77398| Discriminator Loss>Generator Loss: False
Epoch 1/25| Step 100| Discriminator Loss:1.65748| Generator Loss:0.44912| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 150| Discriminator Loss:1.28680| Generator Loss:1.32677| Discriminator Loss>Generator Loss: False
Epoch 1/25| Step 200| Discriminator Loss:1.38144| Generator Loss:0.39361| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 250| Discriminator Loss:1.10408| Generator Loss:0.57143| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 300| Discriminator Loss:1.10593| Generator Loss:1.44764| Discriminator Loss>Generator Loss: False
Epoch 1/25| Step 350| Discriminator Loss:1.17191| Generator Loss:0.72391| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 400| Discriminator Loss:1.05970| Generator Loss:1.53237| Discriminator Loss>Generator Loss: False
Epoch 1/25| Step 450| Discriminator Loss:1.26226| Generator Loss:0.43938| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 500| Discriminator Loss:2.51380| Generator Loss:0.12603| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 1/25| Step 550| Discriminator Loss:1.14403| Generator Loss:2.26527| Discriminator Loss>Generator Loss: False
Epoch 1/25| Step 600| Discriminator Loss:1.28109| Generator Loss:0.48434| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 650| Discriminator Loss:1.86044| Generator Loss:0.23403| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 700| Discriminator Loss:1.09914| Generator Loss:0.53206| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 750| Discriminator Loss:1.50224| Generator Loss:0.34164| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 800| Discriminator Loss:1.61584| Generator Loss:0.27157| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 850| Discriminator Loss:2.84781| Generator Loss:0.07667| Discriminator Loss>Generator Loss: True
Epoch 1/25| Step 900| Discriminator Loss:0.81449| Generator Loss:0.68416| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 950| Discriminator Loss:1.35132| Generator Loss:0.39967| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1000| Discriminator Loss:1.88071| Generator Loss:0.19776| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 2/25| Step 1050| Discriminator Loss:0.70310| Generator Loss:1.83482| Discriminator Loss>Generator Loss: False
Epoch 2/25| Step 1100| Discriminator Loss:0.97791| Generator Loss:1.03689| Discriminator Loss>Generator Loss: False
Epoch 2/25| Step 1150| Discriminator Loss:1.38768| Generator Loss:0.37160| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1200| Discriminator Loss:1.36735| Generator Loss:0.35138| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1250| Discriminator Loss:1.90300| Generator Loss:0.21275| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1300| Discriminator Loss:1.20420| Generator Loss:0.51321| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1350| Discriminator Loss:1.49979| Generator Loss:0.30047| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1400| Discriminator Loss:1.36646| Generator Loss:0.38867| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1450| Discriminator Loss:3.80148| Generator Loss:0.05961| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1500| Discriminator Loss:1.86942| Generator Loss:0.21424| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 2/25| Step 1550| Discriminator Loss:1.16075| Generator Loss:0.46141| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1600| Discriminator Loss:1.48808| Generator Loss:0.31177| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1650| Discriminator Loss:2.01443| Generator Loss:0.16845| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1700| Discriminator Loss:0.91048| Generator Loss:0.65859| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1750| Discriminator Loss:1.80440| Generator Loss:0.32349| Discriminator Loss>Generator Loss: True
Epoch 2/25| Step 1800| Discriminator Loss:0.72293| Generator Loss:1.30179| Discriminator Loss>Generator Loss: False
Epoch 2/25| Step 1850| Discriminator Loss:1.38531| Generator Loss:0.36564| Discriminator Loss>Generator Loss: True
Epoch 3/25| Step 1900| Discriminator Loss:0.92353| Generator Loss:0.81584| Discriminator Loss>Generator Loss: True
Epoch 3/25| Step 1950| Discriminator Loss:1.82928| Generator Loss:0.89236| Discriminator Loss>Generator Loss: True
Epoch 3/25| Step 2000| Discriminator Loss:1.70385| Generator Loss:0.25332| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 3/25| Step 2050| Discriminator Loss:0.55386| Generator Loss:1.59470| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2100| Discriminator Loss:0.20705| Generator Loss:2.01187| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2150| Discriminator Loss:0.68743| Generator Loss:0.88460| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2200| Discriminator Loss:0.75678| Generator Loss:0.93948| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2250| Discriminator Loss:2.01370| Generator Loss:0.18652| Discriminator Loss>Generator Loss: True
Epoch 3/25| Step 2300| Discriminator Loss:0.37827| Generator Loss:1.48640| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2350| Discriminator Loss:0.55722| Generator Loss:1.02480| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2400| Discriminator Loss:0.26072| Generator Loss:2.88552| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2450| Discriminator Loss:0.21577| Generator Loss:1.83840| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2500| Discriminator Loss:1.11182| Generator Loss:0.50689| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 3/25| Step 2550| Discriminator Loss:2.28330| Generator Loss:0.17538| Discriminator Loss>Generator Loss: True
Epoch 3/25| Step 2600| Discriminator Loss:0.30318| Generator Loss:1.68569| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2650| Discriminator Loss:0.17326| Generator Loss:2.39253| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2700| Discriminator Loss:0.18839| Generator Loss:2.04202| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2750| Discriminator Loss:0.73359| Generator Loss:2.30456| Discriminator Loss>Generator Loss: False
Epoch 3/25| Step 2800| Discriminator Loss:1.12909| Generator Loss:2.95274| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 2850| Discriminator Loss:1.32483| Generator Loss:0.55016| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 2900| Discriminator Loss:0.80569| Generator Loss:0.68432| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 2950| Discriminator Loss:0.99864| Generator Loss:0.54982| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3000| Discriminator Loss:0.62022| Generator Loss:1.15042| Discriminator Loss>Generator Loss: False

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 4/25| Step 3050| Discriminator Loss:0.39715| Generator Loss:1.69062| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3100| Discriminator Loss:1.04902| Generator Loss:0.56096| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3150| Discriminator Loss:0.78689| Generator Loss:0.71950| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3200| Discriminator Loss:0.04266| Generator Loss:4.79964| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3250| Discriminator Loss:0.22052| Generator Loss:1.84896| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3300| Discriminator Loss:0.03343| Generator Loss:5.06976| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3350| Discriminator Loss:0.25308| Generator Loss:1.68598| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3400| Discriminator Loss:1.09584| Generator Loss:0.52111| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3450| Discriminator Loss:0.29510| Generator Loss:3.02369| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3500| Discriminator Loss:1.26656| Generator Loss:0.43306| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 4/25| Step 3550| Discriminator Loss:0.67711| Generator Loss:1.52611| Discriminator Loss>Generator Loss: False
Epoch 4/25| Step 3600| Discriminator Loss:1.39026| Generator Loss:0.32697| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3650| Discriminator Loss:0.85043| Generator Loss:0.75415| Discriminator Loss>Generator Loss: True
Epoch 4/25| Step 3700| Discriminator Loss:0.62851| Generator Loss:1.09307| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 3750| Discriminator Loss:1.00714| Generator Loss:0.51711| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 3800| Discriminator Loss:1.68398| Generator Loss:0.23640| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 3850| Discriminator Loss:1.03515| Generator Loss:0.50760| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 3900| Discriminator Loss:0.47940| Generator Loss:3.42309| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 3950| Discriminator Loss:0.64534| Generator Loss:0.85154| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4000| Discriminator Loss:1.13304| Generator Loss:0.49128| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 5/25| Step 4050| Discriminator Loss:0.60727| Generator Loss:0.87043| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4100| Discriminator Loss:1.04487| Generator Loss:4.24823| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4150| Discriminator Loss:0.56285| Generator Loss:1.76855| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4200| Discriminator Loss:1.98052| Generator Loss:0.18687| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4250| Discriminator Loss:1.46654| Generator Loss:0.31033| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4300| Discriminator Loss:1.49380| Generator Loss:0.32083| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4350| Discriminator Loss:0.93331| Generator Loss:4.68337| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4400| Discriminator Loss:1.44076| Generator Loss:0.31469| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4450| Discriminator Loss:1.27441| Generator Loss:0.38838| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4500| Discriminator Loss:0.64015| Generator Loss:0.88080| Discriminator Loss>Generator Loss: False

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

Epoch 5/25| Step 4550| Discriminator Loss:0.53373| Generator Loss:0.98596| Discriminator Loss>Generator Loss: False
Epoch 5/25| Step 4600| Discriminator Loss:0.97883| Generator Loss:0.57891| Discriminator Loss>Generator Loss: True
Epoch 5/25| Step 4650| Discriminator Loss:0.32669| Generator Loss:1.74925| Discriminator Loss>Generator Loss: False
Epoch 6/25| Step 4700| Discriminator Loss:2.12074| Generator Loss:0.17004| Discriminator Loss>Generator Loss: True
Epoch 6/25| Step 4750| Discriminator Loss:2.62060| Generator Loss:0.31437| Discriminator Loss>Generator Loss: True
Epoch 6/25| Step 4800| Discriminator Loss:1.95394| Generator Loss:0.35288| Discriminator Loss>Generator Loss: True
Epoch 6/25| Step 4850| Discriminator Loss:0.12177| Generator Loss:2.42359| Discriminator Loss>Generator Loss: False
Epoch 6/25| Step 4900| Discriminator Loss:1.41569| Generator Loss:0.32735| Discriminator Loss>Generator Loss: True
Epoch 6/25| Step 4950| Discriminator Loss:1.51534| Generator Loss:0.30685| Discriminator Loss>Generator Loss: True
Epoch 6/25| Step 5000| Discriminator Loss:0.30966| Generator Loss:1.46120| Discriminator Loss>Generator Loss: False

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 1)

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 32
z_dim = 128
learning_rate = 0.002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
L1: (?, 14, 14, 64)
L2: (?, 7, 7, 128)
L3: (?, 4, 4, 256)
L4: (?, 2, 2, 512)
L5: (?, 1)
Epoch 1/10| Step 50| Discriminator Loss:1.76753| Generator Loss:0.28755| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 100| Discriminator Loss:1.49257| Generator Loss:0.70981| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 150| Discriminator Loss:1.55677| Generator Loss:0.71989| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 200| Discriminator Loss:1.16356| Generator Loss:0.69585| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 250| Discriminator Loss:1.25665| Generator Loss:0.55249| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 300| Discriminator Loss:1.07509| Generator Loss:0.79609| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 350| Discriminator Loss:1.45598| Generator Loss:0.43053| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 400| Discriminator Loss:1.39017| Generator Loss:0.76210| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 450| Discriminator Loss:1.41141| Generator Loss:0.50907| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 500| Discriminator Loss:1.28549| Generator Loss:0.53407| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 550| Discriminator Loss:1.50267| Generator Loss:0.71413| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 600| Discriminator Loss:1.29711| Generator Loss:0.66394| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 650| Discriminator Loss:1.51908| Generator Loss:0.37006| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 700| Discriminator Loss:1.62076| Generator Loss:0.71734| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 750| Discriminator Loss:1.41481| Generator Loss:0.64885| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 800| Discriminator Loss:1.36750| Generator Loss:0.43582| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 850| Discriminator Loss:1.43225| Generator Loss:0.57197| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 900| Discriminator Loss:1.49728| Generator Loss:0.35571| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 950| Discriminator Loss:1.33606| Generator Loss:0.55546| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1000| Discriminator Loss:1.23537| Generator Loss:0.58291| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 1050| Discriminator Loss:1.52906| Generator Loss:0.58759| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1100| Discriminator Loss:1.61901| Generator Loss:0.40588| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1150| Discriminator Loss:1.56540| Generator Loss:0.47838| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1200| Discriminator Loss:1.23435| Generator Loss:0.89122| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1250| Discriminator Loss:1.90110| Generator Loss:0.28677| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1300| Discriminator Loss:1.70489| Generator Loss:0.26111| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1350| Discriminator Loss:1.71491| Generator Loss:0.27530| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1400| Discriminator Loss:1.23647| Generator Loss:0.78907| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1450| Discriminator Loss:1.35922| Generator Loss:0.68524| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1500| Discriminator Loss:1.39406| Generator Loss:0.55696| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 1550| Discriminator Loss:1.08635| Generator Loss:0.74175| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1600| Discriminator Loss:1.22084| Generator Loss:1.06009| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1650| Discriminator Loss:1.52106| Generator Loss:0.91748| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1700| Discriminator Loss:1.43053| Generator Loss:0.78879| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1750| Discriminator Loss:1.33662| Generator Loss:0.53638| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1800| Discriminator Loss:1.21779| Generator Loss:0.82690| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1850| Discriminator Loss:1.36004| Generator Loss:0.64008| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1900| Discriminator Loss:1.54630| Generator Loss:0.36063| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 1950| Discriminator Loss:1.45333| Generator Loss:0.54557| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2000| Discriminator Loss:1.07917| Generator Loss:0.77885| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 2050| Discriminator Loss:1.46918| Generator Loss:0.58253| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2100| Discriminator Loss:1.43152| Generator Loss:0.47063| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2150| Discriminator Loss:1.23902| Generator Loss:0.76048| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2200| Discriminator Loss:1.44924| Generator Loss:0.58685| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2250| Discriminator Loss:1.39095| Generator Loss:0.80492| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2300| Discriminator Loss:1.56059| Generator Loss:0.37487| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2350| Discriminator Loss:1.44417| Generator Loss:0.83608| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2400| Discriminator Loss:1.27422| Generator Loss:0.64521| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2500| Discriminator Loss:1.25718| Generator Loss:0.78816| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 2550| Discriminator Loss:1.46173| Generator Loss:0.41982| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2600| Discriminator Loss:1.50879| Generator Loss:0.41334| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2650| Discriminator Loss:1.44821| Generator Loss:0.48716| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2700| Discriminator Loss:1.43289| Generator Loss:0.64960| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2750| Discriminator Loss:1.27570| Generator Loss:0.66322| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2800| Discriminator Loss:1.28847| Generator Loss:0.82363| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2850| Discriminator Loss:1.11807| Generator Loss:0.59540| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2900| Discriminator Loss:1.28025| Generator Loss:0.63520| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 2950| Discriminator Loss:1.26037| Generator Loss:1.01826| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3000| Discriminator Loss:1.49356| Generator Loss:0.46906| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 3050| Discriminator Loss:1.12551| Generator Loss:0.89297| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3100| Discriminator Loss:1.42001| Generator Loss:0.53623| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3150| Discriminator Loss:1.37367| Generator Loss:0.45331| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3200| Discriminator Loss:1.46071| Generator Loss:0.45307| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3250| Discriminator Loss:1.41632| Generator Loss:0.67120| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3300| Discriminator Loss:1.53075| Generator Loss:0.53914| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3350| Discriminator Loss:1.79845| Generator Loss:0.35804| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3400| Discriminator Loss:1.39618| Generator Loss:0.76675| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3450| Discriminator Loss:1.36434| Generator Loss:0.55684| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3500| Discriminator Loss:1.42122| Generator Loss:0.39529| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 3550| Discriminator Loss:1.60715| Generator Loss:0.47844| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3600| Discriminator Loss:1.43207| Generator Loss:0.44508| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3650| Discriminator Loss:1.56375| Generator Loss:0.41313| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3700| Discriminator Loss:1.73472| Generator Loss:0.29409| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3750| Discriminator Loss:1.36233| Generator Loss:0.49399| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3800| Discriminator Loss:1.17965| Generator Loss:0.56694| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3850| Discriminator Loss:1.92100| Generator Loss:0.22856| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3900| Discriminator Loss:1.32419| Generator Loss:0.57897| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 3950| Discriminator Loss:1.45885| Generator Loss:0.37411| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4000| Discriminator Loss:1.44993| Generator Loss:0.50551| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 4050| Discriminator Loss:1.41156| Generator Loss:0.54442| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4100| Discriminator Loss:1.54380| Generator Loss:0.31541| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4150| Discriminator Loss:1.28051| Generator Loss:0.94073| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4200| Discriminator Loss:1.62090| Generator Loss:0.36271| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4250| Discriminator Loss:2.04202| Generator Loss:0.30523| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4300| Discriminator Loss:1.40950| Generator Loss:0.52632| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4350| Discriminator Loss:1.55132| Generator Loss:0.50121| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4400| Discriminator Loss:1.37769| Generator Loss:0.73543| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4450| Discriminator Loss:1.35276| Generator Loss:0.53020| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4500| Discriminator Loss:1.48867| Generator Loss:0.43016| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Epoch 1/10| Step 4550| Discriminator Loss:1.06482| Generator Loss:0.70562| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4600| Discriminator Loss:1.36620| Generator Loss:0.53831| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4650| Discriminator Loss:1.73314| Generator Loss:0.39186| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4700| Discriminator Loss:1.28498| Generator Loss:0.58260| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4750| Discriminator Loss:1.20204| Generator Loss:0.59858| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4800| Discriminator Loss:1.53662| Generator Loss:0.41319| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4850| Discriminator Loss:1.44556| Generator Loss:0.39048| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4900| Discriminator Loss:1.32866| Generator Loss:0.52132| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 4950| Discriminator Loss:1.30582| Generator Loss:0.50611| Discriminator Loss>Generator Loss: True
Epoch 1/10| Step 5000| Discriminator Loss:1.52037| Generator Loss:0.40210| Discriminator Loss>Generator Loss: True

DL1: (?, 7, 7, 512)
DL2: (?, 14, 14, 256)
DL3: (?, 28, 28, 128)
DL4: (?, 28, 28, 3)

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.